Abstract [en]

Time delays in control systems diminish performance or even cause instability. Additionally, inherent model errors occurring in model based control approaches yield undesired system behavior and further reduce performance. An example of such a system is the digital hydraulic radial piston motor where several cylinders actuated by valves contribute to the output torque. The systems complexity makes precise system modeling difficult and valves to actively control in- and outflow of the cylinders cause undesired delays.

In this work an iterative learning control(ler) (ILC) approach is presented to compensate model uncertainties of the higher level optimal controller and delays caused by the valves. Due to the use of on/off valves discrete inputs are considered. First a detailed valve model is derived for a solenoid on/off valve for the use in simulations. Missing parameters are estimated. The derived model and the estimated parameters accurately describe the valve response. Comparing the model response with measurement data shows this. Iterative learning control is then used to compensate delays and model errors of the system with binary control inputs, varying iterations length and changing reference trajectories. Simulations and hardware-in-the-loop (HIL) experiments show that the method can reliably compensate valve delays and to some extend model uncertainties.